Why are Some Children Left Out? Factors Barring Children from Participating in Extra-Curricular Activities. Conference for New Researchers, University of Montreal, march 28 2008
Bibliographic record
Abstract
Complex pattern of effect: low SES or high risk children benefit more from extracurricular participation (McNeal 1998; Simpkins et al. 2005; Offord et al. 1998 ) • Low SES children have lower participating rate (Barsh and Kleiman, 2002; Bening 2007; Fredericks and Eccles 2006; Mahoney and Cairns 1997) Findings about extra-curricular activities… Limitations in previous study of children's extracurricular participation • Focus on adolescents, little research on younger children • Focus on outcomes, little research on factors • Factors pointed out by previous studies to influence participation -Child gender (individual child level) -Ethnicity/Race (individual child level) -Parent SES (family level) -Neighborhood characteristics (Neighborhood level) -Studies rarely include factors at all three levels into analysis Analytical model Parent's time availability (e.g.mother's employment status, number of children, single-mother) Financial resources Parent's human capital Neighbourhood environment (e.g.available infrastructure, safety and peer influence, playmates) Participation in extracurricular activities Child characteristics Age, gender and ethnicity Ethno-culture (immigrants)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".